With the abundance of multimedia data made available through various means in general and the Internet and world-wide web (WWW) in particular, there is also a need to provide for effective ways of searching for such multimedia data. Searching for multimedia data in general and video data in particular may be challenging at best due to the huge amount of information that needs to be checked. Moreover, when it is necessary to find a specific content of video, existing solutions revert to using various metadata that describes the content of the multimedia data. However, such content may be complex by nature and, therefore, not necessarily adequately documented as metadata.
The rapidly increasing number and size of multimedia databases, accessible for example through the Internet, call for the application of effective means for search-by-content. Searching for multimedia in general and for video data in particular is challenging due to the huge amount of information that has to be classified. Moreover, existing solutions revert to model-based methods to define and/or describe multimedia data. Some other existing solutions can determine whether an image that matches a known image to classify the content in the image. Those solutions cannot, however, may be unable to identify a match if, for example, content within the known image is of a different color, shown at a different angle, and so on.
By its very nature, the structure of such multimedia data may be too complex to be adequately represented by means of metadata. The difficulty arises in cases where the target sought for multimedia data cannot be adequately defined in words, or respective metadata of the multimedia data. For example, it may be desirable to locate a car of a particular model in a large database of video clips or segments. In some cases, the model of the car would be part of the metadata, but in many cases it would not. Moreover, the car may be at angles different from the angles of a specific photograph of the car that is available as a search item. Similarly, if a piece of music, as in a sequence of notes, is to be found, it is not necessarily the case that in all available content the notes are known in their metadata form, or for that matter, the search pattern may just be a brief audio clip.
A system implementing a computational architecture (hereinafter “The Architecture”) typically consists of a large ensemble of randomly, independently, generated, heterogeneous processing cores, mapping in parallel data-segments onto a high-dimensional space and generating compact signatures for classes of interest. The Architecture is based on a PCT patent application number WO 2007/049282 and published on May 3, 2007, entitled “A Computing Device, a System and a Method for Parallel Processing of Data Streams”, assigned to common assignee, and is hereby incorporated by reference for all the useful information it contains.
It would be advantageous to use The Architecture to overcome the limitations of the prior art described hereinabove. Specifically, it would be advantageous to show a framework, a method, a system, and respective technological implementations and embodiments, for large-scale matching-based multimedia deep content classification, that overcomes the well-known limitations of the prior art.